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> ## pairwise comparisons distractor photo race correlates with photos observed
> ## only target photos used for analysis
> ##
> ## August 9, 2019
> ##
> ## Kevin Quinn
> ## University of Michigan
> ##
> 
> library(MCMCpack)
Loading required package: coda
Loading required package: MASS
##
## Markov Chain Monte Carlo Package (MCMCpack)
## Copyright (C) 2003-2019 Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park
##
## Support provided by the U.S. National Science Foundation
## (Grants SES-0350646 and SES-0350613)
##
> set.seed(87415)
> 
> mydata <- read.csv("../ScaleRaceSpring2017clean.csv")
> 
> 
> ## subset data
> mydata <- mydata[mydata$condition %in% c("P1B", "P2W"),]
> 
> ## keep responses and photo IDs but nothing else
> target.inds <- c(6, 7, 10, 12, 15, 17, 19, 20, 22, 23)
> keep.vars <- c(paste("X", target.inds, "_Q58", sep=""),
+                paste("X", target.inds, "a.id", sep=""),
+                paste("X", target.inds, "b.id", sep=""))
> 
> mydata.sub <- mydata[, keep.vars]
> 
> 
> 
> ## reshape into long format
> mydata.sub.long <- reshape(mydata.sub, direction="long",
+                            varying=list(c(1:10), c(11:20), c(21:30)),
+                            v.names=c("Y", "photoID.a", "photoID.b"),
+                            ids=rownames(mydata.sub))
> 
> ## keep only obs with non-missing values for MM
> mydata.sub.long <- na.omit(mydata.sub.long)
> mydata.sub.long <- mydata.sub.long[mydata.sub.long$Y != "",]
> 
> 
> ## convert to character variables
> mydata.sub.long$Y <- as.character(mydata.sub.long$Y)
> mydata.sub.long$photoID.a <- as.character(mydata.sub.long$photoID.a)
> mydata.sub.long$photoID.b <- as.character(mydata.sub.long$photoID.b)
> 
> ## recode Y to match what MCMCpaircompare expects
> for (i in 1:nrow(mydata.sub.long)){
+     if (mydata.sub.long$Y[i] == "the person in photo 1"){
+         mydata.sub.long$Y[i] <- mydata.sub.long$photoID.a[i]
+     }
+     if (mydata.sub.long$Y[i] == "the person in photo 2"){
+         mydata.sub.long$Y[i] <- mydata.sub.long$photoID.b[i]
+     }    
+ }
> 
> mydata.sub.long <- mydata.sub.long[, c(5, 3, 4, 2)]
> 
> mydata.sub.long <- mydata.sub.long[sample(1:nrow(mydata.sub.long),
+                                           size=1000, replace=FALSE),]
> 
> cat("\n\nN =", nrow(mydata.sub.long), "\n\n") 


N = 1000 

> 
> ## starting values and set up
> pnames <- sort(unique(c(mydata.sub.long$photoID.a, mydata.sub.long$photoID.b)))
> 
> raw.counts <- table(mydata.sub.long$Y)
> zero.names <- pnames[!(pnames %in% names(raw.counts))]
> if (length(zero.names > 0)){
+   old.names <- names(raw.counts)
+   raw.counts <- c(rep(0, length(zero.names)), raw.counts)
+   names(raw.counts) <- c(zero.names, old.names)
+ }
> raw.counts <- raw.counts[pnames]
> raw.ranks <- rank(raw.counts)
> theta.start <- 2*(raw.ranks / length(pnames) - 0.5)
> 
> 
> 
> a2.pairwise.out <- MCMCpaircompare(mydata.sub.long,
+                                    theta.constraints=list(mw086.J1="-",
+                                                           mb017.J2="+"),
+                                    theta.start=theta.start,
+                                    alpha.fixed=TRUE,
+                                    burnin=50000, mcmc=1000000, thin=50,
+                                    verbose=10000, seed=14753
+                          )


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> 
> 
> save(a2.pairwise.out, file="MM3.2.a2.pairwise.out.Rda")
> 
> proc.time()
   user  system elapsed 
 87.627   0.714  90.717 
